

Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer (RAG Specialist) on a 6–12 month contract, hybrid in London, paying up to £1100 per day. Key skills include RAG techniques, LLMs, vector databases, and cloud-native environments.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
-
💰 - Day rate
1100
-
🗓️ - Date discovered
September 3, 2025
🕒 - Project duration
More than 6 months
-
🏝️ - Location type
Hybrid
-
📄 - Contract type
Inside IR35
-
🔒 - Security clearance
Unknown
-
📍 - Location detailed
London Area, United Kingdom
-
🧠 - Skills detailed
#GCP (Google Cloud Platform) #Indexing #AWS (Amazon Web Services) #Langchain #"ETL (Extract #Transform #Load)" #Documentation #PyTorch #ML (Machine Learning) #Data Science #Scala #Databases #Azure #Data Ingestion #Deployment #AI (Artificial Intelligence) #Transformers #Data Pipeline #Hugging Face #Cloud #Python
Role description
We're seeking a highly skilled Machine Learning Engineer (RAG Specialist) to join our AI hyperscaler client on a 6–12 month contract. You will play a critical role in designing, building, and optimizing Retrieval-Augmented Generation (RAG) systems to support cutting-edge large language model (LLM) solutions at scale.
This position offers a unique opportunity to work with advanced infrastructure and collaborate with world-class AI teams on next-generation machine learning systems.
Key responsibilities
• Design, implement, and optimize RAG pipelines for large-scale AI/ML deployments.
• Develop and maintain data ingestion, retrieval, and indexing workflows across distributed environments.
• Work with vector databases, embeddings, and information retrieval systems to ensure high performance and low latency.
• Collaborate with researchers, ML engineers, and data scientists to integrate RAG into production-grade LLM applications.
• Contribute to scaling and deployment strategies across a hyperscaler environment (cloud-native).
• Monitor, debug, and fine-tune ML pipelines for robustness and efficiency.
• Produce high-quality documentation and knowledge transfer for internal stakeholders.
Required skills and experience
• Proven expertise in RAG (Retrieval-Augmented Generation) techniques and real-world implementation.
• Strong experience with LLMs, transformers, and embeddings.
• Hands-on experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Milvus, Vespa).
• Deep knowledge of Python and modern ML/AI frameworks (e.g., PyTorch, Hugging Face, LangChain, LlamaIndex).
• Strong background in distributed systems, data pipelines, and cloud-native environments (Azure, AWS, or GCP).
• Previous work with AI hyperscalers or large-scale ML infrastructure is highly desirable.
• Excellent problem-solving skills, with the ability to optimize systems for performance and scalability.
Contract details
• Duration: 6–12 months (initial term with potential extension)
• Location: Hybrid – London-based (1 day onsite per week)
• Rate: Up to £1100 per day (Inside IR35)
• Start Date: Immediate / ASAP
Apply now to join our innovative client and contribute to a fast-paced, dynamic environment.
Your consultant
Cam Dalziel is a recruitment specialist in assembling teams in data, AI, design, and technology across Europe. He engages with top talent and is committed to providing a high-quality service that delivers results.
We're seeking a highly skilled Machine Learning Engineer (RAG Specialist) to join our AI hyperscaler client on a 6–12 month contract. You will play a critical role in designing, building, and optimizing Retrieval-Augmented Generation (RAG) systems to support cutting-edge large language model (LLM) solutions at scale.
This position offers a unique opportunity to work with advanced infrastructure and collaborate with world-class AI teams on next-generation machine learning systems.
Key responsibilities
• Design, implement, and optimize RAG pipelines for large-scale AI/ML deployments.
• Develop and maintain data ingestion, retrieval, and indexing workflows across distributed environments.
• Work with vector databases, embeddings, and information retrieval systems to ensure high performance and low latency.
• Collaborate with researchers, ML engineers, and data scientists to integrate RAG into production-grade LLM applications.
• Contribute to scaling and deployment strategies across a hyperscaler environment (cloud-native).
• Monitor, debug, and fine-tune ML pipelines for robustness and efficiency.
• Produce high-quality documentation and knowledge transfer for internal stakeholders.
Required skills and experience
• Proven expertise in RAG (Retrieval-Augmented Generation) techniques and real-world implementation.
• Strong experience with LLMs, transformers, and embeddings.
• Hands-on experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Milvus, Vespa).
• Deep knowledge of Python and modern ML/AI frameworks (e.g., PyTorch, Hugging Face, LangChain, LlamaIndex).
• Strong background in distributed systems, data pipelines, and cloud-native environments (Azure, AWS, or GCP).
• Previous work with AI hyperscalers or large-scale ML infrastructure is highly desirable.
• Excellent problem-solving skills, with the ability to optimize systems for performance and scalability.
Contract details
• Duration: 6–12 months (initial term with potential extension)
• Location: Hybrid – London-based (1 day onsite per week)
• Rate: Up to £1100 per day (Inside IR35)
• Start Date: Immediate / ASAP
Apply now to join our innovative client and contribute to a fast-paced, dynamic environment.
Your consultant
Cam Dalziel is a recruitment specialist in assembling teams in data, AI, design, and technology across Europe. He engages with top talent and is committed to providing a high-quality service that delivers results.